The objective is to design, build, and clinically assess ParkinStep, an innovative neurotechnology that integrates wireless motion sensing, automated home-based Parkinson's disease (PD) gait and balance assessment, and deep brain stimulation (DBS) parameter estimation using a web-based database model to integrate data across clinical centers. Following DBS surgery, programming of stimulation settings is performed in the clinic to optimize treatment effectiveness. Upper extremity motor symptoms such as tremor, bradykinesia (slowed movements), and rigidity are typically evaluated in response to DBS settings. Although gait and balance are critical components to quality of life measures and lower extremity dysfunction can be disabling, current in-clinic evaluations are limited. The time required for stimulation to fully impact gait and balance may exceed the typical time of a programming session. While the effect of DBS on tremor may be almost immediate, obtaining feedback on stimulation effectiveness, gait and balance may require in excess of three hours when stimulation settings are adjusted. From a programming standpoint, the effect of stimulation settings on gait and balance is less understood than with other motor symptoms, possibly due to the complex motor circuits involved in gait. ParkinStep will address these concerns through home monitoring to fully capture the effect of stimulation and possible fluctuations experienced throughout the day. In addition, the patient data collected during home tests will be continuously compiled into an online HIPAA-compliant database to automatically output suggested stimulation settings. Therefore, clinicians, independent of geographic location or programming experience, will have access to this service for improved DBS programming outcomes of gait and balance. The clinical system resulting from Phase II development will 1) allow high compliance home monitoring of PD gait and balance symptoms in response to DBS, 2) allow clinicians to optimize PD response to DBS using a stimulation parameter estimation model, and 3) address geographic disparities of patient clinical access to evaluate gait and balance impairment. This will be achieved by modifying our existing Kinesia HomeView system for lower extremity wear and high compliance in the home settings. The system will be evaluated in a multi-center clinical study to populate the online database to train the DBS parameter estimation model. Finally, the developed model will be used in a clinical impact study to determine whether the ParkinStep system can achieve improved gait and balance response to DBS over traditional methods.